64 research outputs found
Distributed cooperative control for economic operation of multiple plugāin electric vehicle parking decks
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138231/1/etep2348.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138231/2/etep2348_am.pd
Fully distributed AC power flow (ACPF) algorithm for distribution systems
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163883/1/stg2bf00044.pd
Distanceāoriented hierarchical control and ecological driving strategy for HEVs
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163948/1/els2bf00154.pd
Two- stage stochastic operation framework for optimal management of the water- energy- hub
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166193/1/gtd2bf02716.pd
Large-Signal Stability Criteria in DC Power Grids with Distributed-Controlled Converters and Constant Power Loads
The increasing adoption of power electronic devices may lead to large
disturbance and destabilization of future power systems. However, stability
criteria are still an unsolved puzzle, since traditional small-signal stability
analysis is not applicable to power electronics-enabled power systems when a
large disturbance occurs, such as a fault, a pulse power load, or load
switching. To address this issue, this paper presents for the first time the
rigorous derivation of the sufficient criteria for large-signal stability in DC
microgrids with distributed-controlled DC-DC power converters. A novel type of
closed-loop converter controllers is designed and considered. Moreover, this
paper is the first to prove that the well-known and frequently cited
Brayton-Moser mixed potential theory (published in 1964) is incomplete. Case
studies are carried out to illustrate the defects of Brayton-Moser mixed
potential theory and verify the effectiveness of the proposed novel stability
criteria
Day-ahead electricity demand forecasting competition: post-COVID paradigm
The COVID-19 related shutdowns have made significant impacts on the electric grid operation worldwide. The global electrical demand plummeted around the planet in 2020 continuing into 2021. Moreover, demand shape has been profoundly altered as a result of industry shutdowns, business closures, and people working from home. In view of such massive electric demand changes, energy forecasting systems struggle to provide an accurate demand prediction, exposing operators to technical and financial risks, and further reinforcing the adverse economic impacts of the pandemic. In this context, the āIEEE DataPort Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm" was organized to support the development and dissemination state-of-the-art load forecasting techniques that can mitigate the adverse impact of pandemic-related demand uncertainties. This paper presents the findings of this competition from the technical and organizational perspectives. The competition structure and participation statistics are provided, and the winning methods are summarized. Furthermore, the competition dataset and problem formulation is discussed in detail. Finally, the dataset is published along with this paper for reproducibility and further research
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